Diagnosis of chronic rejection

ABSTRACT

Methods are disclosed for, early diagnosing chronic rejection (CR) in a transplanted subject, monitoring CR in a transplanted subject at risk of developing CR, preventing, inhibiting, reducing or treating CR in a transplanted subject, or identifying agents for use in the prevention, inhibition, reduction or treatment of CR, based on genes which are differentially expressed in transplant biopsy tissues, before any overt clinical or histological manifestation of CR is detected in the transplanted subject.

This invention relates to a method of monitoring the status of atransplanted tissue or organ in a recipient. In particular, theinvention relates to the use of gene expression analysis to determineearly prediction of chronic allograft rejection.

Chronic allograft rejection (CR) is the major cause for the failure oflong-term graft survival. In contrast to treatable acute rejectionepisodes, chronic rejection is not reversible to date by any treatmentwhen histologically detected, is not proven to be preventable by anyimmunosuppressive regimen and its pathogenesis is not fully understoodbut involving immunological as well as non-immunological factors.Characteristic for chronic rejection in all solid organ grafts is aconcentric arterial intimal thickening by vascular remodeling. Kidneyallografts with chronic rejection exhibit in addition pronouncedparenchymal fibrosis and glomerular sclerosis: clinically, CR ismanifested by a progressive decline in renal function, accompanied byproteinuria and hypertension.

Attempts to identify biomarkers in transplantation research have mainlybeen hypothesis-driven and lead to the characterization of individualgenes, polymorphisms, biochemical or histopathological features.However, most of these studies were performed on tissue samples thatalready showed overt signs of disease and thus only revealed anassociation with the future severity of the disease.

There is a need to have a reliable tool for early prognosis andfollow-up of CR, particularly early prognosis of CR before any overtclinical or histological manifestation, e.g. within the first year posttransplantation; such an aid would be valuable e.g. for the optimisationof current treatment regimens and the design of clinical trials,including with new CR inhibiting agents.

The present invention relates to the identification of genes which aredifferentially expressed in transplant biopsies, e.g. renal biopsies,prior to the onset of CR in patients who will develop CR after thebiopsy was taken, and patients that will not. The resulting geneexpression pattern of a subset of the genes allows a highlystatistically significant predictability of the occurrence of CR. Forexample, the genes identified in renal biopsies post transplantationbefore CR became histologically manifest, are indicated in Tables 1(upregulated genes) and 2 (downregulated genes) and a subset ofpreferred genes in Table 3. The complete sequences of these 65 genesdisclosed in this application are available using the GenBank accessionnumber shown in Tables 1 to 3. The sequences as shown under thecorresponding GenBank accession number are incorporated herein byreference.

The genes identified according to the invention are useful predictivebiomakers for the prognosis of CR in transplanted subjects. Anyselection, of at least one, of these genes can be utilized as surrogatebiomarker for early prognosis of CR. In particularly useful embodiments,a plurality of these genes, e.g. the 10 genes of Table 3 in case ofrenal biopsies, can be selected and their mRNA expression monitoredsimultaneoulsy to provide expression profiles for use in variousaspects.

Accordingly, the invention provides the use of a gene as listed in Table1, 2 or 3 as an early biomarker for chronic transplant rejection, e.g.as a biomarker for CR before any overt clinical or histologicalmanifestation, e.g. within the first year post transplantation.

In a further aspect, the invention provides the use of a gene as listedin Table 1, 2 or 3, excluding FAS gene missing exon 4, retinoblastomabinding protein 7, prohibitin and connective tissue growth factor, as abiomarker for chronic transplant rejection.

In a further embodiment, the levels of the gene expression products(proteins) can be monitored in various body fluids, including, but notlimited to, blood plasma, serum, lymph, urine, stool and bile, or inbiopsy tissues. This expression product level can be used as surrogatemarkers for early diagnosis of CR and can provide indices of therapyresponsiveness. An example is e.g. the protein encoded by the ConnectiveTissue Growth Factor (GenBank accession number X78947).

Accordingly, the invention also provides the use of an expressionproduct of (e.g. a protein encoded by) a gene as listed in Table 1, 2 or3 as an early biomarker for chronic transplant rejection, e.g. as abiomarker for CR before any overt clinical or histologicalmanifestation, e.g. within the first year post transplantation.

In a further aspect, the invention provides the use of an expressionproduct of (e.g. a protein encoded by) a gene as listed in Table 1, 2 or3, excluding FAS gene missing exon 4, retinoblastoma binding protein 7,prohibitin and connective tissue growth factor, as a biomarker forchronic transplant rejection.

The methods of the present invention may be performed in vitro, e.g. thelevels of biomarkers may be analysed In tissues or fluids extracted orobtained from a transplanted subject.

Methods of detecting the level of expression of mRNA are well-known inthe art and include, but are not limited to, reverse transcription PCR,real time quantitative PCR, Northern blotting and other hybridizationmethods.

A particularly useful method for detecting the level of mRNA transcriptsobtained from a plurality of the disclosed genes involves hybridizationof labeled mRNA to an ordered array of oligonucleotides. Such a methodallows the level of transcription of a plurality of these genes to bedetermined simultaneously to generate gene expression profiles orpatterns. The gene expression profile derived from the biopsy obtainedfrom the transplanted subject at risk of developing CR can be comparedwith the gene expression profile derived from the sample obtained from atransplanted subject that will not develop CR.

In a further embodiment, measuring expression profiles of one or aplurality of these genes or encoded proteins could provide valuablemolecular tools for examining the efficacy of drugs for inhibiting, e.g.preventing or treating, CR. Changes in the expression profile from abaseline profile while the transplanted patient is exposed to therapy.Accordingly, this invention also provides a method for screening atransplanted subject to determine the likelihood that the subject willrespond to the CR therapy, methods for the identification of agents thatare useful in treating a transplanted subject having CR signs andmethods for monitoring the efficacy of certain drug treatments for CR.

In one aspect, the invention features a (e.g. in vitro) method ofidentifying at least one gene which is differentially expressed In anallograft of a given tissue type prior to the onset of CR in a testtransplanted subject compared to

i) a gene expression baseline profile originating from the same tissuetype of a control transplanted subject who is known not to develop CR;or

ii) a gene expression baseline profile originating from the testtransplanted subject at the date of the transplantation.

The term “differentially expressed” refers to a given allograft geneexpression level and is defined as an amount which is substantiallygreater or less than the amount of the corresponding baseline expressionlevel.

In another aspect, the invention provides a (e.g. in vitro) method ofearly diagnosing CR in a test transplanted subject by detecting adifferentially expressed gene in a given allograft tissue sample. Forexample, the method may comprise

a) taking as a baseline value the level of mRNA expression correspondingto or protein encoded by at least one gene, e.g. as identified in Tables1 and 2 or 3, the gene originating from a specific allograft tissuebiopsy of a control transplanted subject who is known not to develop CR;

b) detecting a level of mRNA expression corresponding to or proteinencoded by the at least one gene identified in a) in an allograft tissuebiopsy of the same tissue type as in a) obtained from a testtransplanted subject within the first year post-transplantation; and

c) comparing the first value with the second value, wherein a firstvalue lower or higher than the second value predicts that the testtransplanted subject is at risk of developing CR.

According to another embodiment, the (e.g. in vitro) method may alsocomprise

a) detecting a level of mRNA expression corresponding to or proteinencoded by at least one gene, e.g. as identified in Tables 1 and 2 or 3,in an allograft tissue biopsy obtained from the donor, preferably aliving donor, at the day of transplantation,

b) detecting a level of mRNA expression corresponding to or proteinencoded by the at least one gene identified in a) in an allograft tissuebiopsy obtained from a patient within the first yearpost-transplantation,

c) comparing the first value with the second value, wherein a firstvalue lower or higher than the second value predicts that thetransplanted subject is at risk of developing CR.

In steps b) above, the level of mRNA or protein encoded is preferablydetected within 4 to 7 months post-transplantation, more preferablyaround 6 months post-transplantation.

By prior to the onset of CR or early diagnosis of CR is meant before anyovert clinical or histological manifestation of CR is detected in thetransplanted subject.

The method of early diagnosing CR according to the invention may also beapplied to maintenance patients, i.e. patients who have beentransplanted more than one year ago. Accordingly, biopsies are performedand the level of mRNA expression corresponding to at least one gene iscompared to the level in the reference control values to identifypatients that will develop CR during the next couple of months.

In another aspect, the invention provides a method for monitoring, e.g.preventing or inhibiting or reducing or treating CR in a transplantedsubject at risk of developing CR with a CR inhibitor (e.g. a smallmolecule, an antibody or other therapeutic agent or candidate agent).Monitoring the influence of agents (e.g. drug compounds) on the level ofexpression of a marker of the invention can be applied not only in basicdrug screening, but also in clinical trials. For example, theeffectiveness of an agent to affect marker expression can be monitoredin clinical trials of transplanted subjects receiving treatment for theinhibition of CR.

Such a method comprises:

a) obtaining a pre-administration sample from a transplanted subjectprior to administration of the agent,

b) detecting the level of expression of mRNA corresponding to or proteinencoded by the at least one gene in the pre-administration sample,

c) obtaining one or more post-administration samples from thetransplanted patient,

d) detecting the level of expression of mRNA corresponding to or proteinencoded by the at least one gene in the post-administration sample orsamples,

e) comparing the level of expression of mRNA or protein encoded by theat least one gene in the pre-administration sample with the level ofexpression of mRNA or protein encoded by the at least one gene in thepost-administration sample or samples, and

f) adjusting the agent accordingly.

For example, increased or decreased administration of the agent may bedesirable to change the level of expression of the at least one gene tohigher or lower levels than detected. In above method, the agent canalso be administered alone or in combination with other agents in acombined therapy, preferably with immunosuppressive agents and/or agentseffective in CR.

Accordingly, incorporation of gene expression profiling data from humanbiopsies, e.g. human renal protocol biopsies, will help improve thepatient selection process during clinical trials aimed at both treatmentand prevention of the progression towards CR.

In a yet other aspect, the invention further provides a method foridentifying agents for use in the prevention, inhibition, reduction ortreatment of CR comprising monitoring the level of mRNA expression of atleast one gene or protein encoded as disclosed above.

In a further aspect, the invention provides a method for preventing,inhibiting, reducing or treating CR in a transplant subject in need ofsuch treatment comprising administering to the subject a compound thatmodulates the synthesis, expression or activity of one or more genes orgene products, as disclosed in Tables 1, 2 or 3, so that at least onesymptom of CR is ameliorated.

In a further aspect, the invention provides a compound (e.g. a smallmolecule, an antibody or other therapeutic agent or candidate agent)which modulates the synthesis, expression of activity of one or moregenes or gene products identified above (e.g. a gene identified in Table1, 2 or 3) for use as a medicament, e.g. for the prevention or treatmentof CR in a transplanted subject.

In a further aspect, the invention provides the use of a compound (e.g.a small molecule, an antibody or other therapeutic agent or candidateagent) which modulates the synthesis, expression of activity of one ormore genes or gene products identified above (e.g. a gene identified inTable 1, 2 or 3) for prevention or treatment of CR in a transplantedsubject.

In a further aspect, the invention provides the use of a compound (e.g.a small molecule, an antibody or other therapeutic agent or candidateagent) which modulates the synthesis, expression of activity of one ormore genes or gene products identified above (e.g. a gene identified inTable 1, 2 or 3) for the preparation of a medicament for prevention ortreatment of CR in a transplanted subject.

By transplanted subject is meant a subject receiving tissue or organfrom a donor, preferably from the same species, e.g. kidney, heart,lung, combined heart and lung, liver, pancreas, bowel (e.g., colon,small intestine, duodenum), neuronal tissue, limbs.

Preferably more than one gene, e.g. a set of genes, are used in themethods of the invention. The methods of the invention are particularlypreferred in kidney transplantation.

As already mentioned any selection, of at least one, of the genesindicated in Tables 1, 2 or 3 can be used. Preferably a selection of atleast one gene of Table 1, e.g. ORP150 (oxygen regulated protein 150),thioredoxin, OS-9, NPRL2, HOXB7, G-CSF, BFGF etc and/or at least onegene of Table 2, e.g. prolactin receptor, etc. is used. Preferred genesof Table 3 are e.g.OS-9, NPRL2, HOXB7, G-CSF and/or BFGF. Most of thegenes described here have not been implicated in renal allograftrejection and are associated with various functions. For example, OS9,also termed APRIL (acidic protein rich in leucines), is a member of theacidic nuclear phosphoprotein 32 family, with sequence homology toPHAPI, a putative HLA class II associated protein. OS9 also sharessequence similarity to the proto-oncogenes DEK and SET proteins, linkedto myeloid leukemia. OBCML (opiate-binding protein/cell adhesionmolecule-like) has only been described to be expressed in certainregions of the cerebellum, never in kidney. The tumor suppressor geneNPRL2, which shows sequence similarity to the yeast nitrogen permeaseregulator gene, has been shown to be located in the human chromosomalregion 3p21.3, as one of 25 tumor suppressor genes within this regionthat are involved in the development of lung and breast cancer.

Gene expression profiles can be generated using e.g. the Affymetrixmicroarray technology. Microarrays are known in the art and consist of asurface to which probes that correspond in sequence to gene products(e.g. mRNAs, polypeptides, fragments thereof etc.). can be specificallyhybridized or bound to a known position. Hybridization intensity datadetected by the scanner are automatically acquired and processed by theGENECHIP® software. Raw data is normalized to expression levels using atarget intensity of 200.

The transcriptional state of a cell may be measured by other geneexpression technologies known in the art. Several such technologiesproduce pools of restriction fragments of limited complexity forelectrophoretic analysis, such as methods combining double restrictionenzyme digestion with phasing primers (e.g. EP-A1-0 534858), or methodsselecting restriction fragments with sites closest to a defined mRNA end(e.g. Prashar et al, Proc. Nat. Acad. Sci., 93, 659-663, 1996). Othermethods statistically sample cDNA pools, such as by sequencingsufficient bases (e.g. 20-50 bases) in each multiple cDNAs to identifyeach cDNA, or by sequencing short tags (e.g. 9-10 bases) which aregenerated at known positions relative to a defined mRNA end (e.g.Velculescu, Science, 270, 484-487, 1995) pathway pattern.

In another embodiment of the present invention, a protein correspondingto a marker is detected. A preferred agent for detecting a protein ofthe invention is e.g. an antibody capable of binding to the protein,preferably an antibody with a detectable label. Antibodies can bepolyclonal, or preferably, monoclonal. An intact antibody or a fragmentthereof (e.g. Fab or F(ab′)₂ can be used. The term “labeled” is intendedto encompass direct labelling of the antibody by coupling a detectablesubstance to antibody, as well as indirect labeling of the antibody byreactivity with another reagent that is directly labeled. A variety offormats can be employed to determine whether a sample contains a proteinthat binds to a given antibody. Examples of such formats include e.g.enzyme immunoassay, radioimmunoassay, Western blot analysis and ELISA.

In a preferred embodiment, the computation steps of the previous methodsare implemented on a computer system or on one or more networkedcomputer systems in order to provide a powerful and convenient facilityfor forming and testing models of biological systems. The computersystem may be a single hardware platform comprising internal componentsand being linked to external components. The internal components of thiscomputer system include processor element interconnected with mainmemory. The external components include mass data storage. This massstorage can be one or more hard disks. Other external components includeuser interface device, which can be a monitor and keyboards, togetherwith pointing device or other graphic input devices. Typically, thecomputer system is also linked to other local computer systems, remotecomputer systems or wide area communication networks, e.g. Internet.This network link allows the computer system to share data andprocessing tasks with other computer systems.

Loaded into memory during operation of this system are several softwarecomponents which are both standard in the art and special to the instantinvention. These software components collectively cause the computersystem to function according to the methods of this invention. Thesesoftware components are typically stored on mass storage or on removablemedia, e.g. floppy disks or CD-ROM. The software component representsthe operating system, which is responsible for managing the computersystem and its network interconnections, preferably the methods of thisinvention are programmed in mathematical software packages, which allowsymbolic entry of equations and high-level specification of processing,including algorithms to be used, and thereby freeing a user of the needto procedurally program individual equations or algorithms.

In preferred embodiments, the analytic software component actuallycomprises separate software components that interact with each other.Analytic software represents a database containing all data necessaryfor the operation of the system. Such data will generally include, butis not limited to, results of prior experiments, genome data,experimental procedures and cost, and other information, which will beapparent to those skilled in the art. Analytic software Includes a datareduction and computation component comprising one or more programswhich execute the analytic methods of the invention. Analytic softwarealso includes a user interface which provides a user of the computersystem with control and input of test network models and, optionally,experimental data. The user interface may comprise a drag-and-dropinterface for specifying hypotheses to the system. The user interfacemay also comprise means for loading experimental data from the massstorage component, from removable media or from a different computersystem communicating with the instant system over a network.

The invention also provides a process for preparing a databasecomprising at least one of the markers set forth in this invention, e.g.mRNAs. For example, the polynucleotide sequences are stored in a digitalstorage medium such that a data processing system for standardizedrepresentation of the genes that identify early prognosis of CR. Thedata processing system is useful to analyze gene expression between twotissue samples taken at different time point, e.g. at thetransplantation day and post-transplantation. The isolatedpolynucleotides are sequenced. The sequences from the samples may becompared with the sequence(s) present in the database using homologysearch techniques. Alternative computer systems and methods forimplementing the analytic methods of this invention will be apparent toone skilled in the art and are intended to be comprehended within theaccompanying claims.

Identification of Prognostic Markers of CR

As a part of a randomized, multicenter, double-blind, double-dummy,parallel group study, serial renal protocol biopsies are taken at thetime of transplantation (baseline), then 6 months and 12 months aftertransplantation.

After RNA extraction from these biopsies, the overall yield of total RNAranges from only around 10 ng to around 1,5 μg. Most of the samplescontain less than 30 ng total RNA, which is far below the minimal amountof 1 to 5 μg that commercially available RNA labeling kits and methodsneed for subsequent microarray experiments. In order to obtainsufficient amounts of RNA for microarray experiments, linear RNAamplification is performed. Briefly, this method involves subsequentrounds of cDNA and aRNA synthesis, which amplifies the original amount20-30 fold per round. 90 RNA samples are amplified and labeled. aRNAsare hybridized to Affymetrix HG U95A v2 chip containing oligonucleotideprobes of about 12,000 human genes and analyzed.

Tissue Homogenization

All liquid nitrogen flash-frozen biopsy samples are stored in cryotubesat −80° C. Immediately after the addition of 70011 homogenization buffer(ABI lysis buffer/PBS 1:1) the homogenization step is performed bydipping the rod of a Polytron rotor/stator homogenizer PT 3100 into thetissue containing buffer and running the homogenizer at full speed for30 seconds. If after this time remnant tissue pieces are visible, theprocedure is repeated until homogenicity is achieved. Hereafter thehomogenate is stored at −80° C. until it is used in the RNA extractionstep.

Homogenate Pre-Filtration and RNA Extraction

Pre-filtration of the homogenate and RNA extractions are performed bythe ABI 6700 Biorobot workstation (Applied Biosystems, USA). Tissuehomogenates are filled into the wells of a 96-deep-well plate, andplaced in the filtrate position of the 6700 workstation. A tissuepre-filter tray is placed into the purification carriage and locked intoposition. The instrument door is closed, and the workstation software islaunched.

The RNA extraction procedure includes a sample transfer step, afiltration step, a washing step, and an elution step. The sampletransfer step, In which the pre-filtered homogenate is transferred fromthe 96 deep-well plate to the RNA purification tray includes a primarytransfer of 550 μl solution. Before the second transfer, 150 μlhomogenization buffer (Applied Biosystems lysis buffer/PBS 1:1) is addedto each well in the deep-well plate, mixed three times and then 150 μlare transferred from there to the purification tray. The filtration stepis carried out by applying a vacuum pressure of 80% for 180 seconds. Thewashing steps are performed as follows:

Step 1: washing solution 1, 400 μl, vacuum pressure 80% for 180 seconds,two times;

Step 2: washing solution 2, 500 μl, vacuum pressure 80% for 180 seconds,once;

Step 3: washing solution 2, 300 μl, vacuum pressure 60% for 120 seconds,two times.

A pre-elution vacuum of 90% pressure is applied for 300 seconds.Hereafter the elution step is performed by the addition of 120 μlelution solution (Applied Biosystems), and the application of a 100%vacuum-pressure for 120 seconds. The RNA samples are collected in96-well plates (Applied Biosystems). The eluates are split into twoaliquots of equal volume. One aliquot is stored at −80° C., the otheraliquot is used for RNA amplification and GeneChip analysis.

RNA Amplification

Prior to the RNA amplification procedure, all RNA eluates are treatedwith RNeasy kit chemistry (Qiagen) to further clean the RNA from remnantsalt or other substances that may inhibit the amplification efficiency.The volume of the aliquot is adjusted to 100 μl with RNase-free water.350 μl buffer RLT is added and mixed thoroughly. 250 μl ethanol(96-100%) is added, and mixed thoroughly. The sample (700 μl) is appliedto an RNeasy mini column, placed in a 2 ml collection tube. After a 15second centrifugation step at more than 10,000 rpm, the flow-through isdiscarded. The RNeasy column is transferred into a new 2 ml collectiontube. 500 μl buffer RPE is pipetted onto the column, the tube closed andcentrifuged for 15 seconds at more than 10,000 rpm to wash the column.The flow-through is discarded. Another 500 μl buffer RPE is added to thecolumn and the tube is centrifuged for 2 minutes at more than 10,000 rpmto dry the silica-gel membrane. To elute, the RNeasy column istransferred to a new 1,5 ml collection tube and 30 μl RNase-free wateris added directly onto the membrane. After 1 minute incubation, the tubeis centrifuged for 1 minute at more than 10,000 rpm to elute. Thiselution step is repeated once to get a total elution volume of 60 μl.The RNA is quantified by the Ribogreen method (Molecular Probes, Inc,USA). About 10 ng total RNA of each sample is used in three rounds ofRNA amplification. All enzymes and buffers for the amplificationprocedure are purchased from Invitrogen, Inc. (Carlsbad, Calif., USA)unless explicitly mentioned. 10 ml total RNA are incubated with 10 pmolT7-polydT primer [5′-GGCCAGTGMTTGTMTACGACTCACTATAGGGAGGCGG(T)₂₄](Genset, Inc.) in a volume of 11 μl at 70° C. for 10 minutes, then at42° C. for 5 minutes. The first strand reaction is carried out in avolume of 20 μl by the addition of 200 units SuperScript II in thepresence of first strand buffer, 10 mM DTT, 0.5 mM dNTP mixture, and 1μl RNase inhibitor (Ambion, Inc.) with a 42° C.-incubation for 1 hr. Thesecond strand synthesis is performed in 150 μl with 40 units E. coli DNApolymerase I in 1× second strand buffer, 0.2 mM dNTPs, 10 units E. coliDNA ligase and 2 units RNaseH. After a 2-hour incubation at 16° C., thedouble-stranded DNA is blunt-ended by the addition of 8 units T4 DNApolymerase for 10 minutes at 16° C. The double-stranded DNA product ispurified with a QIAquick PCR purification kit (Qiagen) and eluted in 50μl elution buffer. For only one round of amplification the volume of theeluate is reduced to dryness under vacuum, resuspended in 22 μlnuclease-free water, and then used in the RNA labelling reaction asdescribed below. For additional rounds of amplification, the eluate isreduced to dryness under vacuum, resuspended in 8 μl nuclease-freewater, and subjected to an in-vitro transcription reaction with theAmbion MEGAscript kit, following the manufacturer's instructions for a20 μl reaction volume. After a 3-hour incubation at 37° C. the RNA ispurified with the RNeasy kit system (Qiagen). The RNA is eluted in 30 μlRNase free water, reduced to dryness under vacuum and resuspended in 11μl nuclease-free water.

The second round of RNA amplification started with the addition of 1 μl0.1 mg/ml random hexamer primers followed by a 10-minute incubation at70° C. The reaction mixture is chilled on ice and then incubated at roomtemperature for 10 minutes, at which point the first strand synthesisreaction is started by the addition of 200 units SuperScript II, 20units RNase Inhibitor, 0.5 mM dNTPs and 10 mM DTT in the presence offirst strand reaction buffer. The mixture is incubated at 37° C. for 1hour. A 20-minute RNase H treatment (2 units) at 37° C. lead to thedegradation of the residual RNA. RNase H is heat-inactivated at 94° C.for 2 minutes and the mixture is chilled on ice. The second-strandsynthesis is initiated by the addition of 100 pmol T7-polydT primer (seeabove) and incubation at 70° C. for 5 minutes, followed by 42° C. for 10minutes. The second-strand synthesis is performed as described above,and the cDNA is purified with the QIAquick PCR purification kit(Qiagen). If this is the final round of amplification, the volume of theeluate is reduced to dryness under vacuum and resuspended in 22 μlnuclease-free water, followed by an in-vitro RNA labelling procedure(see below).

A third round of amplification is prepared by reducing the eluate todryness under vacuum and resuspended in 8 μl nuclease-free water beforeit is then subjected to the procedure identical to that of a secondround of amplification. We observed a 15 to 30-fold increase in aRNA ineach round of amplification, resulting in 5×10⁵ to 1.9×10⁶-foldamplification of messenger RNA after three rounds (assuming 3.3%poly(A)+ RNA within the initial pool of total RNA). Labelled RNAs arefractionated at 94° C. for 35, 25, or 20 minutes for single (1×), double(2×), or triple (3×) amplified RNAs, respectively. Shorter incubationtimes for 2× RNA and 3× RNA are chosen to avoid complete degradation ofthe RNA.

The RNA biotinylation step involved the use of the High-Yield RNALabelling Kit (Enzo Diagnostics, NY, USA; P/N 900182) following themanufacturer's instructions. The following ingredients are mixed in aninitial step:

-   22 μl aRNA    -   4 μl 10× HY reaction buffer,    -   4 μl 10× Biotin Labelled Ribonucleotides,    -   4 μl 10× DTT,    -   4 μl RNase inhibitor mix,    -   2 μl 20× T7 RNA polymerase.

The mixture is incubated at 37° C. for 3-4 hours. The labelled aRNA ispurified using RNeasy chemistry (Qiagen) following the manufacturer'sinstructions. The elution volume is 60 μl, 2 μl are used to determinethe RNA concentration spectrophotometically by absorbance at 260 nm.

RNA Fragmentation

15 μg labelled aRNA is fragmented in a volume of 20 μl by the additionof 4 μl 5× MES Fragmentation buffer and RNase free water. The mixture isincubated for 20 minutes at 94° C.

12× MES Fragmentation Buffer (for 1000 ml): 70.4 g MES free acid (1.22MMES, 0.89 M [Na⁺] (2-(N-Morpholino)ethanesulfonic acid (SIGMA, P/NM5287) 193.3 g MES sodium salt (Sigma, P/N M3885) 800 ml DEPC water

Filter through a 0.2 μm filter, the pH should be between 6.5 and 6.7without adjustment.

Microarray Hybridization Mix

The hybridization is carried out in a volume of 300 μl. Fragmented aRNAis mixed with 150 μl 2× MES hybridization buffer, 3 μl herring sperm DNA(10 mg/ml), 3 μl BSA (50 mg/ml), 3 μl 948b control oligonucleotide (5nM), and 3 μl 20× Eukaryotic Hybridization Controls (Affymetrix). DEPCwater is added to 300 μl final volume.

2× MES Hybridization Buffer (for 500 ml): 217 ml DEPC water 200 ml 5MNaCl  82 ml 12X MES

Filter through 0.2 μm filter.

Then add: 1.0 ml 10% Triton X-100. Store at room temperature.

Microarray Pre-Treatment

The microarray is incubated at 45° C. for 15 minutes. The array chamberis filled with freshly prepared pre-treatment solution, prewarmed to 45°C.

Pre-Treatment Solution (300 μl Per Microarray) 294 μl  1X MEShybridization buffer 3 μl Acetylated BSA (50 mg/ml) (Gibco BRL LifeTechnologies, P/N 15561-020) 3 μl Herring sperm DNA (10 mg/ml)(Promega/Fisher scientific, P/N D1811)Microarray Hybridization

While the microarrays are being pre-treated at 45° C., the hybridizationmix is incubated at 99° C. for 5 minutes. After a centrifugation for 5minutes at 14,000 rpm the supernatant is transferred to a new Eppendorftube and incubated at 45° C. for 5 minutes. The pre-treatment solutionis removed from the microarray chamber and replaced with thehybridization mix, avoiding bubbles. The septa of the plastic cartridgeare covered with tape and the cartridge is placed in an oven at 45° C.with the glass front facing down. The hybridization is continued for 16to 18 hours.

Washing Procedure

The hybridization mix is removed from the probe array and set aside in amicrocentrifuge tube. 280 μl 1× MES hybridization buffer is added to thechamber and a fluidics wash is performed on a GeneChip Fluidics Station400 using 6× SSPE-T buffer.

6× SSPE-T wash buffer (1000 ml)

-   300 ml 20× SSPE (BioWhittaker, P/N 16-010Y)-   699 ml water-   Filter through 0.2 μm filter. Add 1 ml 10% Triton X-100

After the fluidics wash the SSPE-T buffer is removed from the chamberand filled with stringent wash buffer, avoiding bubbles.

Stringent Wash Buffer (1000 ml):

-   83.3 ml 12× MES buffer-   5.2 ml 5M NaCl-   1 ml 10% Tween 20-   910.5 ml water

Filter through 0.2 μm filter. Add 1 ml 10% Triton X-100.

The microarray cartridges are layed face up in a 45° C. incubation ovenfor 30 minutes. The stringent buffer is removed and the array is rinsedwith 200 μl 1× MES hybridization buffer.

The 1× MES hybridization buffer is completely removed, the array chamberfilled with SAPE stain, and incubated at 37° C. for 15 minutes.

SAPE Stain (600 μl):

-   300 μl 2× MES hybridization buffer-   288 μl water-   6 μl BAS (50 mg/ml)-   6 μl SAPE (1 mg/ml) (Molecular probes, P/N 15230-147)

After 15 minutes the SAPE stain solution is removed, the chamber filledwith 200 μl 1× MES hybridization buffer, and a fluidics wash isperformed. The SSPE-T solution is removed from the microrarray chamberand replaced with 300 μl AB stain.

AB Stain (300 μl:   150 μl 2X MES hybridization buffer 146.25 μl water   3 μl BSA (50 mg/ml)  0.75 μl biotinylated antibody (500 μg/ml)(Vector laboratories, P/N BA-0500)

The cartridge is incubated at 37° C. for 30 minutes, the AB stain isreplaced with 200 μl 1× MES hybridization buffer, and a fluidics wash isperformed. After the wash step, the SSPE-T solution is removed, thechamber is filled with SAPE stain, and incubated at 37° C. for 15minutes. The SAPE stain is replaced with 200 μl 1× MES hybridizationbuffer and a fluidics wash is performed. The septa are covered with tapeto prevent buffer leakage.

Microarrays are scanned on Affymetrix GeneArray® scanners. Raw data setsare normalized by scaling 75%—quantile of all probe sets of each chip toa target intensity of 200.

Separation Method

Statistical analysis is performed with S-Plus (Insightful, Inc., USA)and GeneSpring® (Silicon Genetics, USA). Average difference values ofless than 10 are rounded to 10. Low expression levels (between 10 and50) are kept to ensure not to lose any possible pattern. Standardparametric and non-parametric statistics (Student's t-test, Wilcoxon'srank sum test) are applied.

More particularly, an algorithm is developed to separate the data for aparticular gene for the CR and control group, a distinction being madebetween the CR group which has lower expression values than the controlgroup and the CR group which has higher expression values than thecontrol group. In addition to having separate expression ranges, theseparation gap between the groups is maximized. Thus the gap between aspecified quantile (Q) of the 2 data sets (i.e the 1−α quantile of thegroup with lower expression levels and a quantile of the group withhigher expression levels with 0<α<0.5).

The statistic to measure how well each gene separates the two groups isgiven asS=max (q _(α,cr) −q _(−α,control) , q _(α,control) −q _(1−α,cr), 0)where q_(α,cr) denotes the a quantile of the CR group with 0<α<0.5 (andtherefore 1−α<0.5). Similarly, control or cr denotes the control group.

The 0 is added to the evaluation of S because both terms,q_(α,cr)−q_(1−α,control), and q_(α,control)−q_(1−α,cr), can be negative.In this case, measuring the difference does not make any sense, as thegroups overlay too much. In other words, the 1−α quantile of the onegroup must be below the a quantile of the other.

An algorithm with following settings is used:

Threshold (for noise) set to T=10.

Threshold (for minimum expression) set to T₂=50.

The minimum percentage of signals above threshold T₂ is set in eachgroup to P=40%.

The minimum percentage of signals above threshold T₂ over all groupscombined is set to P=50% (such that a higher percentage in one group canto some extent compensate a lower percentage in the other group).

The percentage of signals below T₂ is determined for each gene,individually for each group (CR, control).

All genes that do not fulfill all minimum percentage thresholds arediscarded.

The statistic S is calculated for each gene.

The genes with S>0 are returned, sorted by S.

As a first pass filter all genes containing a too large proportion oflow signals are excluded. A gene is included in the dataset if

-   -   both groups, CR and control, contain at least 40 percent values        above the threshold T₂=50 (at least 4 out of 8 and 4 out of 9        samples).    -   the combined dataset contains at least 50 percent values above        the threshold T=10 (at least 9 out of 17 samples).

In a second pass, all expression values below 10 are set to 10 to reducerandom variation. (The Affymetrix algorithm used to derive theexpression levels can generate negative values). Low levels (between 10and 50) are preferred to ensure not to lose any pattern that mightoccur.

In GeneSpring™, each gene is normalized to itself by creating asynthetic positive control for that gene, which is the mean of allvalues of that gene in a dataset, and dividing all measurements for thatgene by this positive control, assuming it is at least 0.01.

TaqMan Primer Probe Design

TaqMan assays should be designed to the region of a gene that hybridizesto the corresponding probe set of the HG-U95Av2 microarray. This regionis called target sequence. Using the Netaffx™ software (Affymetrix,Inc), the target sequence of a probe set is identified. The targetsequence is then imported into the program Primer Express (AppliedBiosystems), and the primer/probe selection is performed by the programwith the following conditions:

Primer TM (melting temperature) should be between 58° C. and 60° C.Optimally it should be 59° C., with a maximum TM difference of 2° C.

The primer GC (GTP,CTP) content should be between 20% at the minimum and80% at the maximum, avoiding any 3′ GC clamps.

The optimal primer length should be 20 residues, but can range form 9 to40 residues.

The amplicon requirements should be that the minimum TM is 0° C., themaximum TM 92° C.

The amplicon should have a minimal length of 50 residues, a maximallength of 150.

TaqMan probe criteria are that the probe TM must be at least 0° C.greater than the PCR primer TM, and the probe should not begin with a G(GTP) residue. If the target sequence is too short to identify anyTaqMan assay matching the above mentioned criteria, the sequence isaligned to the entire sequence of the gene (using standard software suchas GCG, Wisconsin Package, Accelrys, San Diego, Calif.) and a longerstretch of DNA is selected, encompassing the target sequence. Sequencesof the forward primer, reverse primer and the TaqMan probe for each geneare listed in Table 4.

To identify the genes that separate best the chronic rejection groupfrom the control group standard techniques are applied, e.g. t andWilcoxon statistics, and a newly derived measure to find well-separatedgroups (with a large separation gap in between) is included. The measure(Q15/85 and Q20/80) extends the concept of separation to finding a goodseparation gap. It is based on measuring the distance between theα-quantile of the CR group and the (1−α)-quantile of the control groupand vice versa. If the (α, 1−α) ranges of both groups do not overlap,the maximum distance is reported, otherwise 0. Applying the measures toeach gene individually delivers a measure for the separation of the 2groups. The Q20/80 method identified 65 genes and the 015/85 method 16genes with complete separation of the (20%, 80%) and (15%, 85%) quantileranges, respectively.

Comparing the genes detected to the ordered t- and Wilcoxon statisticidentified that 10 of those genes are ranked among the 100 with mostextreme t- and Wilcoxon statistic. The gene identifiers and annotationsare given e.g. in table 3.

Almost all genes identified show expression levels above the lowthreshold of 50, mostly even much higher.

Applying a different way of evaluating the power of a set of genes toseparate the two groups of samples, a cluster analysis of all 65 genesthat are common to both, the t statistic list and the Wilcoxon list isperformed. The cluster is prepared in two steps. First, a gene clusteris prepared by standard correlation, then an array cluster by Pearsoncorrelation. To make a tree, GeneSpring calculates the correlation foreach gene with every other gene in the set. Then it takes the highestcorrelation and pairs those two genes, averaging their expressionprofiles. GeneSpring then compares this new composite gene with all ofthe other unpaired genes. This is repeated until all of the genes havebeen paired. At this point the minimum distance and the separation ratiocome into play. Both of these affect the branching behavior of the tree.The minimum distance deals with how far down the tree discrete branchesare depicted. The number specified in the minimum distance boxdetermines the minimum separation considered significant between genes.This reduces meaningless structure at the base of the tree. Decreasingminimum distance increases the ‘branchiness’ of the tree. Defaultminimum distance is 0.001. A value smaller than 0.001 has very littleeffect, because most genes are not correlated more closely than that. Ahigher number will tend to lump together more genes into a group, makingthe groups less specific.

This number should be between 0 and 1. The separation ratio determineshow large the correlation difference between groups of clustered geneshas to be for the groups to be considered discrete groups and not bejoined together. Increasing separation increases the ‘branchiness’ ofthe tree. The default separation ratio is 0.5, but it can range from 0.0to 1.0. At a separation ratio of 0, all gene expression profiles can beregarded as identical.

The Pearson correlation is very similar to the Standard correlation,except it measures the angle of expression vectors for genes A and Baround the mean of the expression vectors (for example, the mean of theexpression values constituting the profiles for Gene A and Gene B).Generally the mean of the expression vectors will be positive sinceexpression values are based on concentrations of mRNA. Using the Pearsoncorrelation more negative correlations are obtained than from theStandard correlation. It is worth noting that the Pearson correlationgives you almost the same correlations as the Standard correlation whenthey are both performed on the logarithms of the genes' expressionvalues. This is how to compute a Pearson correlation: calculate the meanof all elements in vector a. Then subtract that value from each elementin a. Call the resulting vector A. Do the same for b to make a vector B.Pearson Correlation=A.B/(|A∥B|)

The gene cluster is prepared by performing a standard correlationanalysis with a separation ratio value of 0.5 and a minimum distancevalue of 0.001. For the microarray cluster a Pearson correlation is usedwith a separation ratio of 1.0 and a minimum distance value of 0.001.Accordingly, the set of 65 genes separates the two patient groupsperfectly. The distance between the two groups is rather small. This wasexpected since all patients were clinically and histologically healthyat the point these biopsies were drawn.

By applying above method the expression levels of about 12,000transcripts in serial renal allograft protocol biopsies from 17transplant patients have been monitored. Demographic and clinicalcharacteristics of all patients in this study are listed in Table 5. Onegroup of patients developed CR within 6 months after the timepoint thebiopsy was taken, the other group did not. Using the set of genes asdisclosed, preferably the set of 10 genes as indicated in Table 3, asidentified by detailed statistical analysis of the biopsy RNA expressionprofiles, the occurrence/non occurrence of chronic rejection waspredicted in 15 out of these 17 patients (>88%). Furthermore, the set ofdiscriminator genes was also able to predict that a month 12 biopsybelonged to a patient that developed CR until month 18. TABLE 1 List ofgenes (with GenBank accession numbers) which are upregulated in pre-CRgroup GenBank accession Affymetrix fold Wilcoxon t-test number probe setdescription change p-value p-value X07696 37582_at cytokeratin 15/KRT157.96 0.0003 0.0192 AB005666 36843_at GTPase-activating protein 6.930.0064 0.0284 U65785 33863_at ORP150 mRNA 6.54 0.0193 0.0051 M1693737618_at homeobox c1 protein/hoxB7 5.73 0.0009 0.0197 Z37166 35292_atBAT1 mRNA (DEAD family) 5.50 0.0266 0.0163 U48231 33549_at bradykinin B1receptor 4.88 0.0030 0.0108 U77968 34652_at member of the bHLH-PAS 4.550.0047 0.0059 family AJ000480 35597_at C8FW phosphoprotein 4.30 0.00460.0089 AF040708 40499_r_at NPRL2/similar to yeast NPR2 4.28 0.00410.0058 nitrogen permease AJ011497 38482_at claudin-7 4.34 0.0193 0.0044Z70519 37644_s_at FAS gene missing exon 4 4.41 0.0064 0.0103 U4163536996_at OS-9 2.70 0.0011 0.0388 U45448 33535_at ATP-gated ion channel2.69 0.0104 0.0167 AB010414 39893_at G-protein gamma 7 2.54 0.00300.0186 X98411 35132_at myosin-IF 2.47 0.0152 0.0240 AB011082 36408_atORCTL4 2.38 0.0289 0.0230 X63546 1613_s_at tre oncogene 3.05 0.00680.0048 S80864 35955_at putative protein 3.01 0.0289 0.0189 AI82789536224_g_at IMAGE: 2350347 2.98 0.0152 0.0406 U13897 40246_at humanhomolog of Drosophila 2.88 0.0071 0.0072 discs large protein U1679937669_s_at Na, K-ATPase beta-1 subunit 2.88 0.0211 0.0163 X14445 1855_atint-2 (FGF-3) 2.92 0.0107 0.0406 AI653621 36992_at thioredoxin 3.120.0101 0.0108 U79251 41093_at OBCML, opioid binding 3.48 0.0047 0.0166protein/cell adhesion molecule- like M59818 34223_at granulocytecolony-stimulating 3.58 0.0095 0.0310 factor receptor (G-CSFR-1) D110861506_at interleukin 2 receptor gamma 3.71 0.0149 0.0146 chain NM0028931515_at retinoblastoma binding protein 7 4.01 0.0106 0.0281 AL04944933997_at DKFZp586B1722 4.08 0.0107 0.0333 AL049228 31985_atDKFZp564N1716 4.10 0.0249 0.0228 X64116 32698_at poliovirus receptor1.92 0.0107 0.0134 S85655 36592_at prohibitin 1.94 0.0152 0.0098 W2646931377_r_at EST ID 32f4 from retina cDNA 2.23 0.0152 0.0208 randomlyprimed sublibrary AB020638 33226_at KIAA0876 2.25 0.0152 0.0402

TABLE 2 List of genes (with GenBank accession numbers) earlydownregulated in pre-CR group GenBank accession Affymetrix fold Wilcoxont-test number probe set description change p-value p-value U5663740910_at capping protein alpha subunit 4.16 0.0010 0.0036 isoform 1D14110 1276_g_at RNA binding protein 3.76 0.0030 0.0135 M2419434609_g_at homologue; putative (G-protein) 3.71 0.0046 0.0168 X5677739720_g_at ZP3 3.48 0.0095 0.0301 U94747 38171_at WD repeat protein;similar to 3.49 0.0172 0.0282 petunia AN11 AB018313 39130_at KIAA07703.47 0.0211 0.0469 U66059 32795_at T cell receptor beta locus 3.300.0203 0.0410 M93651 40189_at SET gene 5.97 0.0028 0.0141 AJ00581440343_at hoxA7 5.03 0.0055 0.0549 X75861 33988_at TEGT 5.27 0.00710.0470 X78947 36638_at connective tissue growth factor 5.22 0.02080.0489 L05095 31708_at ribosomal protein L30 10.83  0.0211 0.0579 U5211231873_at ARD1 subunit homolog 3.01 0.0046 0.0106 AF031416 35960_at IKKbeta 3.03 0.0149 0.0076 M31661 1079_g_at prolactin (PRL) receptor 3.090.0180 0.0231 Z25749 34646_at ribosomal protein S7 2.95 0.0072 0.0185AF034176 32218_at clone ntcon5 contig 2.87 0.0106 0.0285 U90904 38452_atclone 23773 2.87 0.0104 0.0430 D38048 39060_at proteasome subunit z 2.800.0193 0.0356 AI743134 41246_at similar to glia derived nexin 2.810.0212 0.0383 precursor U66078 33972_r_at DAZLA 2.58 0.0048 0.0053AB023154 35369_at KIAA0937 2.66 0.0072 0.0133 AI540925 41206_r_atPEC1.2_15_A02.r cDNA 2.26 0.0072 0.0145 U07132 519_g_at Ner-I steroidhormone receptor 2.29 0.0149 0.0267 AA527880 35774_r_at NDUFB7 2.380.0152 0.0356 AI828210 38592_s_at IMAGE: 2421832 2.35 0.0212 0.0165U77327 32970_f_at CD30 1.82 0.0107 0.0211 U40282 35365_atintegrin-linked kinase 1.80 0.0212 0.0154 AF029778 32137_at jagged 2(Notch ligand) 1.84 0.0289 0.0422 M92287 1795_g_at cyclin D3 (CCND3)1.89 0.0289 0.0193 AF042379 39918_at GCP2 2.00 0.0107 0.0205 X56468409_at 14.3.3tau 2.12 0.0152 0.0210

TABLE 3 Subset of 10 genes from tables 1 and 2 with the most significantdifferential expression patterns for the pre-CR and the control group.The expression pattern of eight genes are validated by TaqMan ™real-time Q-PCR. fold fold accession change change number namedescription (HG-U95) (Q-PCR) upregulated in preCR group at month 6X07696 KRT15 cytoskeletal structural protein 7.96 2.36 M16937 hoxB7homeodomain family of DNA binding proteins 5.73 2.55 AF040708 NPRL2candidate tumor suppressor gene 21 protein 4.28 4.60 U41635 OS9 acidic(leucine-rich) nuclear phosphoprotein 2.70 4.20 AB010414 G-protein γ 7guanine nucleotide binding protein 2.54 4.99 U79251 OBCML immunoglobulinprotein superfamily member 3.48 20.89 AL049449 DKFZp5586B1722uncharacterized 4.08 4.21 W26469 EST ID 32f4 uncharacterized 2.23 nddownregulated in preCR group at month 6 AJ005814 hoxA7 homeodomaintranscription factor −5.03 1.86 M31661 PRLR prolactin receptor −3.09−32.31nd: not done due to limited sequence information.

TABLE 4 Sequences and labels of all probes and primers used inTaqMan ™ assays. GenBank accession # Name Sequence X07696 KRT155′-GGCTTTGCATGCGCTCTATT-3′ 5′-GCTGCATCTCCTTGCTCCA-3′5′-FAM-CCCCTCTGCCTCTCCCCACCTTC-TAMRA-3′ M16937 HoxB75′-GGAGCCCCAAAACCTACCA-3′ 5′-AAGCAAGAAGCAGCAGCCA-3′5′-FAM-TCGCGTGTTCCCCAAGCGC-TAMRA-5′ AF040708 NPR25′-TGGGAGTTACCTGAGGGAAGC-3′ 5′-GATTGGCAGTGCCCCATG-3′5′-FAM-AGACCCTTTATGTCTCTCAGGAGCCCTGGA-TAMRA-3′ U41635 OS95′-GCAAGGAGGGCAGGACACT-3′ 5′-CAAACATCACTAAGGGCAGGTG-3′5′-FAM-CAGGCACTGAGCAAGCAGGCCC-TAMRA-3′ AB010414 G-protein γ75′-TGGCCTTCTCAGTTTGGGC-3′ 5′-TTCAGTTATTCCGAACGGGAA-3′5′-FAM-AAAGGGATGGAGGCTTTACGGCCA-TAMRA-3′ U79251 OBCML5′-CTGAGCCACCTTTGCTGTCTT-3′ 5′-TTTGAATCCCAGGCAACTTTG-3′5′-FAM-TCTCCTGGGACGAGAAGGACTCATCCA-TAMRA-3′ AL049449 DKFZp586B17225′-AACTTGCCAATTCTGTGAATGTTATT-3′ 5′-GGGACATGTTACCCAATCACAA-3′5′-FAM-ATTTAAAAAGCTGGGTCTGTAATGGGAGGCATT-TAMRA-3′ AJ005814 HoxA75′-TGGAAATTCTGCTCACTTCTTGC-3′ 5′-TCTGATGTCATGGCCAAATTTG-3′5′-FAM-CTTGCTTGCTTCTCTGGTGGGCTTCC-TAMRA-3′ M31661 PRLR5′-GACACTACTAAAGCTCCCAGCTCC-3′ 5′-TTCTGGAATCAGCTGCTGGA-3′5′-FAM-TTCATGCTCCATTTTTAACCACTTGCCTCTT-TAMRA-3′

TABLE 5 Demographics of the recipients and donors. recipient endstagemonth 12 HLA renal histology BANFF donor mismatch at patient age genderethnicity disease AR diagnosis grading age gender ethnicity type of txA-B-DR loci A 41 F Ot GN/GD 1 1 mild 22 M Ot CadHB 1-1-2 B 27 M Ca GN/GD0 1 mild 26 F Ca LivUnrel 1-2-2 C 42 M Ca DM 0 1 mild 39 F Ca CadHB1-1-0 D 62 M Ca GN/GD 1 1 mild 53 M Ca CadHB 1-1-1 E 54 M Ca GN/GD 0 1mild 44 F Ca CadHB 2-1-2 F 50 M Ca other 0 1 mild 55 F Ca LivRel 1-1-1 G27 M Ca other 0 1 mild 55 F Ca Liv rel 1-1-1 H 39 M Ca Unkn 0 1 moderate47 F Ca LivUnrel 1-2-2 I 49 F Ca PyN/IN 0 1 mild 47 F Ca LivUnrel 2-2-1J 19 F Bl Unkn 0 0 none 42 F Bl LivRel 1-1-0 K 50 F Ca Unkn 0 0 none 23F Ca LivRel 0-0-0 L 53 M Ca DM 0 0 none 62 M Ca CadHB 1-0-1 M 47 F CaPCKD 1 0 none 51 M Ot CadHB 2-0-0 N 30 F Ot GN/GD 0 0 none 59 M OtLivRel 0-1-1 O 37 F Ca Htn/Nsc 0 0 none 42 F Ca LivRel 1-1-1 P 30 M BlUnkn 1 0 none 27 M Bl LivRel 2-2-2 Q 21 F Ot GN/GD 0 0 none 43 F OtLivRel 0-0-0

Recipients A-I developed CR between month 6 and month 12, patients J-Qremained healthy. AR: number of acute rejection episodes;

Gender: F, female; M, male. Ethnicity: Ot, oriental; Ca, Caucasian; BI,black. End stage renal disease: GN/GD, glomerulonephritis/glomerulardisease; PyN/IN, pyelonephritis/interstitial nephritis; PCKD, polycystickidney disease; Htn/Nsc, hypertension/nephrosclerosis; Vsc, vasculitis;DM=diabetes mellitus; OD/R, obstructive disorder/reflux; Unk, unknownorigin. AR: number of acute rejection episodes. Month 12 histologydiagnosis: 1, Chronic rejection positive; 0, no rejection. Type oftransplant: CadHB, cadaveric heart beating; CadNHB, cadaveric non-heartbeating; LivRel, living related; LivUnrel, living unrelated.

1. A method of early diagnosing chronic rejection (CR) in a transplantedsubject comprising a) taking as a baseline value the level of mRNAexpression corresponding to or protein encoded by at least one gene, thegene originating from a specific allograft tissue biopsy of atransplanted subject who is known not to develop CR; b) detecting alevel of mRNA expression corresponding to or protein encoded by the atleast one gene identified in a) in an allograft tissue biopsy of thesame tissue type as in a) obtained from a patient within the first yearpost-transplantation; and c) comparing the first value with the secondvalue, wherein a first value lower or higher than the second valuepredicts that the transplanted subject is at risk of developing CR.
 2. Amethod according to claim 1, wherein the baseline value a) is obtainedby detecting a level of mRNA expression corresponding to or proteinencoded by at least one gene in an allograft tissue biopsy obtained fromthe donor at the day of transplantation.
 3. A method for monitoring CRin a transplanted subject at risk of developing CR comprising a)obtaining a pre-administration sample from a transplanted subject priorto administration of a CR inhibiting agent, b) detecting the level ofexpression of mRNA corresponding to or protein encoded by the at leastone gene in the pre-administration sample, c) obtaining one or morepost-administration samples from the transplanted patient, d) detectingthe level of expression of mRNA corresponding to or protein encoded bythe at least one gene in the post-administration sample or samples, e)comparing the level of expression of mRNA or protein encoded by the atleast one gene in the pre-administration sample with the level ofexpression of mRNA or protein encoded by the at least one gene in thepost-administration sample or samples, and f) adjusting the agentaccordingly.
 4. A method for preventing, inhibiting, reducing ortreating CR in a transplanted subject in need of such treatmentcomprising administering to the subject a compound that modulates thesynthesis, expression or activity of one or more genes or gene productsas identified in claim 1, so that at least one symptom of CR isameliorated.
 5. A method for identifying agents for use in theprevention, inhibition, reduction or treatment of CR comprisingmonitoring the level of mRNA expression of one or more genes or geneproducts as identified in claim
 1. 6. A method according to claim 1,wherein the transplanted subject is a kidney transplanted subject.
 7. Amethod according to claim 5, wherein the genes are selected from thegroup of genes having the following GenBank accession numbers X07696,AB005666, U65785, M16937, Z37166, U48231, U77968, AJ000480, AF040708,AJ011497, Z70519, U41635, U45448, AB010414, X98411, AB011082, X63546,S80864, A1827895, U13897, U16799, X14445, A1653621, U79251, M59818,D11086, NM002893, AL049449, AL049228. X64116, S85655, W26469, AB020638,U56637, D14110, M24194, X56777. U94747. AB018313, U66059. M93651,AJ005814. X75861, X78947, L05095, U52112, AF031416. M31661, Z25749,AF034176, U90904, D38048, A1743134, U66078, AB023154, A1540925, U07132,AA527880, A1828210. U77327, U40282, AF029778, M92287, AF042379, X56468,X07696, M16937, AF040708. U41635, AB10414, U79251, AL049449, W26469,AJ005814 and M31661.
 8. A method according to claim 1, wherein the levelof expression of the gene expression is assessed by detecting thepresence of a protein corresponding to the gene expression product.
 9. Amethod according to claim 8, wherein the presence of the protein isdetected using a reagent which specifically binds to the protein.
 10. Amethod according to claim 1, wherein the level of mRNA expression of oneor more genes is detected by techniques selected from the groupconsisting of Northern blot analysis, reverse transcription PCR and realtime quantitative PCR.
 11. A method according to claim 1, wherein thelevel of mRNA expression of a set of genes is detected. 12-14.(canceled)